Efficiently measuring complexity on the basis of real-world data (Q280651)
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scientific article; zbMATH DE number 6578385
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Efficiently measuring complexity on the basis of real-world data |
scientific article; zbMATH DE number 6578385 |
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Efficiently measuring complexity on the basis of real-world data (English)
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10 May 2016
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Summary: Permutation entropy, introduced by Bandt and Pompe, is a conceptually simple and well-interpretable measure of time series complexity. In this paper, we propose efficient methods for computing it and related ordinal-patterns-based characteristics. The methods are based on precomputing values of successive ordinal patterns of order \(d\), considering the fact that they are ``overlapped'' in \(d\) points, and on precomputing successive values of the permutation entropy related to ``overlapping'' successive time-windows. The proposed methods allow for measurement of the complexity of very large datasets in real-time.
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permutation entropy
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ordinal patterns
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efficient computing
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complexity
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